Independent screening for single-index hazard rate models with ultra-high dimensional features

TL;DR

This paper introduces a fast, independent screening method for ultra-high dimensional survival data, ensuring relevant features are identified with high probability under certain conditions, and extends it with an iterative approach for complex data.

Contribution

It proposes a novel survival equivalent of correlation screening with theoretical sure screening guarantees for single-index hazard models, including an iterative version for complex feature structures.

Findings

Method achieves high probability of capturing relevant features.

Simulation studies demonstrate effectiveness.

Application to gene expression data shows practical utility.

Abstract

In data sets with many more features than observations, independent screening based on all univariate regression models leads to a computationally convenient variable selection method. Recent efforts have shown that in the case of generalized linear models, independent screening may suffice to capture all relevant features with high probability, even in ultra-high dimension. It is unclear whether this formal sure screening property is attainable when the response is a right-censored survival time. We propose a computationally very efficient independent screening method for survival data which can be viewed as the natural survival equivalent of correlation screening. We state conditions under which the method admits the sure screening property within a general class of single-index hazard rate models with ultra-high dimensional features. An iterative variant is also described which combines screening with penalized regression in order to handle more complex feature covariance structures. The methods are evaluated through simulation studies and through application to a real gene expression dataset.